Genetic Algorithms in Molecular Modeling
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Table of contents
- Contentsv
- Contributorsix
- Prefacexi
- Chapter 1. Genetic Algorithms in Computer-Aided Molecular Design1
- Abstract1
- Introduction1
- Classes of Search Techniques2
- Mechanics of Simple Genetic Algorithms4
- Applications of Genetic Algorithms in QSAR and Drug Design11
- Software Availability14
- Advantages and Limitations of Genetic Algorithms20
- References21
- Chapter 2. An Overview of Genetic Methods35
- Abstract35
- Introduction35
- Genetic Alphabet and Genes38
- Focusing and Similarity42
- Creating an Initial Population44
- Building a Mating Population45
- Choosing a Parent46
- Mating46
- Mutation Operator50
- Maturation Operator52
- Process Offspring53
- Updating the Population55
- Summary56
- Review of Various Published Algorithms58
- Conclusion64
- References64
- Chapter 3. Genetic Algorithms in Feature Selection67
- Abstract67
- About Feature Selection67
- Application of Genetic Algorithms to Feature Selection68
- Classical Methods of Feature Selection vs Genetic Algorithms69
- Configuration of a Genetic Algorithm for Feature Selection70
- The Hybridization with Stepwise Selection74
- The Problem of Full-Validation77
- Two QSAR Examples78
- Acknowledgements85
- References86
- Chapter 4. Some Theory and Examples of Genetic Function Approximation with Comparison to Evolutionar87
- Abstract87
- Introduction87
- Genetic Function Approximation88
- Comments on the Lack-Of-Fit Measure89
- Nonlinear Modeling92
- GFA versus PLS Modeling98
- Comparison of GFA with other Genetic and Evolutionary Methods100
- Conclusions104
- Acknowledgments106
- References106
- Chapter 5. Genetic Partial Least Squares in QSAR109
- Abstract109
- Introduction109
- Background110
- PLS111
- Genetic Algorithms112
- Genetic Partial Least Squares115
- Outlier Limiting116
- Case Study118
- Conclusion128
- References129
- Chapter 6. Application of Genetic Algorithms to the General QSAR Problem and to Guiding Molecular Di131
- Abstract131
- Introduction and Background132
- Methods132
- Results139
- Concluding Remarks154
- Acknowledgments155
- References156
- Chapter 7. Prediction of the Progesterone Receptor Binding of Steroids Using a Combination of Geneti159
- Abstract159
- Introduction160
- Experimental162
- Results177
- Concluding Remarks189
- References190
- Chapter 8. Genetically Evolved Receptor Models (GERM): A Procedure for Construction of Atomic-Level193
- Abstract193
- Introduction194
- Methods195
- Results and Discussion202
- Conclusion209
- Acknowledgments209
- References209
- Chapter 9. Genetic Algorithms for Chemical Structure Handling an d Molecular Recognition211
- Abstract211
- Introduction212
- 3-D Conformational Search212
- Flexible Ligand Docking219
- Flexible Molecular Overlay and Pharmacophore Elucidation226
- Conclusions238
- Acknowledgements239
- References239
- Chapter 10. Genetic Selection of Aromatic Substituents for Designing Test Series243
- Abstract243
- Introduction243
- Materials and Methods244
- Results and Discussion251
- Conclusion267
- References267
- Chapter 11. Computer-Aided Molecular Design Using Neural Networks and Genetic Algorithms271
- Abstract271
- Introduction272
- The Forward Problem Using Neural Networks275
- Genetic Algorithms for the Inverse Problem286
- Characterization of the Search Space292
- An Interactive Framework for Evolutionary Design297
- Conclusions298
- References300
- Chapter 12. Designing Biodegradable Molecules from the Combined Us e of a Backpropagation Neural Net303
- Abstract303
- Introduction303
- Background304
- Results and Discussion309
- Conclusion312
- References312
- Annexe315
- Index325
- Color Plate SectionColor Plate-1
Genetic Algorithms in Molecular Modeling is the first book available on the use of genetic algorithms in molecular design. This volume marks the beginning of an ew series of books, Principles in Qsar and Drug Design, which will be an indispensible reference for students and professionals involved in medicinal chemistry, pharmacology, (eco)toxicology, and agrochemistry. Each comprehensive chapter is written by a distinguished researcher in the field.
Through its up to the minute content, extensive bibliography, and essential information on software availability, this book leads the reader from the theoretical aspects to the practical applications. It enables the uninitiated reader to apply genetic algorithms for modeling the biological activities and properties of chemicals, and provides the trained scientist with the most up to date information on the topic.
. Extremely topical and timely
. Sets the foundations for the development of
computer-aided tools for solving numerous
problems in QSAR and drug design
. Written to be accessible without prior direct
experience in genetic algorithms
Through its up to the minute content, extensive bibliography, and essential information on software availability, this book leads the reader from the theoretical aspects to the practical applications. It enables the uninitiated reader to apply genetic algorithms for modeling the biological activities and properties of chemicals, and provides the trained scientist with the most up to date information on the topic.
. Extremely topical and timely
. Sets the foundations for the development of
computer-aided tools for solving numerous
problems in QSAR and drug design
. Written to be accessible without prior direct
experience in genetic algorithms